Steady State Learning and Nash Equilibrium
研究玩家通过贝叶斯更新学习对手策略的稳态系统，在长期且耐心的条件下，稳态行动分布近似纳什均衡，对博弈论和学习理论研究者有参考价值。
We study the steady states of a system in which players learn about the strategies their opponents are playing by updating their Bayesian priors in light of their observations.Players are matched at random to play a fixed extensive -form game, and each player observes the realized actions in his own matches, but not the intended off -path play of his opponents or the realized actions in other matches.If lifetimes are long and players are very patient, the steady state distribution of actions approximates those of a Nash equilibrium.